Resolving and understanding the genomic basis of heterogeneous complex traits and diseases

家长助学金 解决和理解异质复杂性状和疾病的基因组基础。 数以百计的基因组学研究暴露了我们对机制关系的理解存在重大差距 基因组变异、细胞过程、组织功能和性状变异之间的关系。父母的目标是 该项目是开发一套计算框架，将大量的基因组和 生物医学数据取得以下三个进展： 方向1：识别和利用复杂特征和疾病的基于机制的亚型。 方向2：描述人类寿命和性别之间的生理学和疾病特征。 方向3：在研究人类特征/疾病的模式生物中寻找类似的背景。 正如我们和其他人所证明的那样，基因组范围的分子网络是分子数据和 知识，并作为强有力的工具，从背景上理解基因在细胞途径中所起的作用， 组织生理学、表型/疾病机制和药物作用。因此，我们父母的一个核心方面 项目是开发多种机器学习方法，以利用分子网络生成 关于基因在定义亚型、年龄/性别差异和 一系列复杂疾病的跨物种类比。 作为这项工作的一部分，我们开发了开源的GenePlexusgitheb.com/krishnanlab/GenePlexus. 运行软件并对我们最先进的方法进行基准测试，以将基因组规模的网络与 有监督的机器学习(ML)以获得关于各种基因属性的准确的新预测(例如， 途径成员或疾病协会；Liu*、Mancuso*等人，2020生物信息学)。 同样，我们的团队致力于将我们的所有其他计算方法提供给更广泛的人 以开放科学软件工具的形式出现的生物医学研究社区。我们已经发布了这样的产品 软件和我们几乎所有的文件。最近的其他例子包括： PecanPy githorb.com/krishnanlab/PecanPy，以获得并行、高效和加速的node2vec。 在转录本中输入未测量基因的Expresto githeb.com/krishnanlab/expresto。 Txt2Onto githeb.com/krishnanlab/Txt2Onto，用于注释基于自由文本元数据的组学样本。 补充项目的目标和当前的原型 GenePlexus：基于网络的机器学习云平台 提议的补充的目标是通过构建一个新的 基于云的GenePllex平台使：i)生物医学/实验研究人员能够无缝地 基于网络的ML在生成可解释的全基因组预测方面的优势，以及ii)计算 研究人员运行基于网络的ML，检索结果，并与现有的数据分析工作流集成。 项目团队，包括PI、一名受过云计算培训的博士后，以及两个专业软件 工程师-在过去的六个月里合作开发了GenePllex平台的原型 (以安装在Microsoft Azure上的Web服务器的形式；图1)。 图1：当前屏幕截图 GenePlexusWeb服务器的原型 实施我们最初的GenePlexusTM软件 Githorb.com/krishnanlab/GenePlexus.当一个 用户上传基因集，GenePlexus会创建 训练ML模型的虚拟机， 利用预先保存的数据。除了……之外 从功能上预测网络中的基因 与用户提供的基因类似，基因丛 提供使用户能够解释 基于相似性的定制ML模型 数以千计的模特接受了培训 预测与生物学相关的基因 过程(来自基因本体论)和疾病 (来自DisGeNET)。除了检索 预测结果带来多重便利 格式，用户可以可视化预测排名靠前的 基因就像一个网络图。

Parent grant Resolving and understanding the genomic basis of heterogeneous complex traits and diseases. Hundreds of genomics studies have exposed major gaps in our understanding of the mechanistic relationships between genomic variation, cellular processes, tissue function, and trait variation. The goal of the parent project is to develop a suite of computational frameworks that integrate massive collections of genomic and biomedical data to make the following three advances: Direction 1: Discern and leverage mechanism-based subtypes of complex traits and diseases. Direction 2: Characterize physiology and disease along the human lifespan and across the sexes. Direction 3: Find analogous contexts in model organisms for studying human traits/diseases. As demonstrated by us and others, genome-wide molecular networks are grand unifiers of molecular data and knowledge, and serve as powerful tools to contextually understand the roles genes play in cellular pathways, tissue physiology, phenotype/disease mechanisms, and drug action. Hence, a central aspect of our parent project is to develop multiple machine learning approaches to leverage molecular networks to generate accurate, testable hypotheses about the roles genes play in defining subtypes, age/sex differences, and cross-species analogs of a range of complex disorders. As part of this work, we have developed GenePlexus github.com/krishnanlab/GenePlexus, an open source software to run and benchmark our state-of-the-art approach for combining genome-scale networks with supervised machine learning (ML) to get accurate novel predictions about various gene attributes (e.g., pathway membership or disease association; Liu*, Mancuso*, et al., 2020 Bioinformatics). Similarly, our group has committed efforts to make all our other computational methods available to the broader biomedical research community in the form of software tools for open science. We have released such software with nearly all our papers. Other recent examples include: ● PecanPy github.com/krishnanlab/PecanPy for parallelized, efficient, and accelerated node2vec. ● Expresto github.com/krishnanlab/Expresto for imputing unmeasured genes in transcriptomes. ● Txt2Onto github.com/krishnanlab/Txt2Onto for annotating –omics samples based on free-text metadata. Goal of the supplement project and current prototype GenePlexus: A cloud platform for network-based machine learning The goal of the proposed supplement is to take our software development to the next level by a building a new cloud-based GenePlexus platform to enable: i) biomedical/experimental researchers to seamlessly take advantage of network-based ML to generate interpretable genome-wide predictions, and ii) computational researchers to run network-based ML, retrieve results, and integrate with existing data analysis workflows. The project team, which includes the PI, a postdoc trained in cloud computing, and two professional software engineers – has worked together over the past six months to develop a prototype of the GenePlexus platform (in the form of a web-server mounted on Microsoft Azure; Fig. 1). Figure 1: Screenshots of the current prototype of the GenePlexus webserver that implements our original GenePlexus software github.com/krishnanlab/GenePlexus. When a user uploads a geneset, GenePlexus creates a virtual machine that trains a ML model, leveraging pre-saved data. In addition to predicting genes in the network functionally similar to the user-supplied genes, GenePlexus provides enables the user to interpret the custom-built ML model in terms of its similarity to thousands of models that were trained to predict genes associated with biological processes (from Gene Ontology) and diseases (from DisGeNET). In addition to retrieving the prediction results in multiple convenient formats, users can visualize the top predicted genes as a network graph.